Voxel selection using submodular function optimization
Post date: Feb 4, 2013 1:24:00 AM
The goal here is to find a “good” subset of voxels B in the brain that collec- tively/jointly forms a representational (dis)similarity matrix (RDM) R that is closest to the ideal sematic (dis)similarity matrix SS obtained from some crite- ria. For instance, SS can be obtained from WordNet, survey, Wikipedia corpus, etc. There are many possible criteria for selecting a good subset, for instance, minimum/maximum number of voxels to be included in the selected voxel subset and/or voxel contiguity in the brain (3D or cortical) space. More detail here.
There are lots of good resources here:
Carlos Guestrin's CMU page.
OPTIMIZING SENSING: FROM WATER TO THE WEB [pdf]
------ Feature selection using MI + submodular function optimization -----
Slides from Bilmes:
http://melodi.ee.washington.edu/~bilmes/ee595a_spring_2011/lecture1.pdf
Another tutorial on SFO:
http://www.di.ens.fr/~fbach/submodular_fbach_mlss2012.pdf
Submodular function page:
http://submodularity.org/icml08/
Tutorial slides on Submodular function optimization (ICML2008) by Carlos:
http://submodularity.org/submodularity-slides.pdf
SFO Toolbox:
http://jmlr.csail.mit.edu/papers/volume11/krause10a/krause10a.pdf
Recommended readings:
- 10] A. Krause and C. Guestrin. Near-optimal nonmyopic value of information in graphical models. In UAI, 2005
- Information gain in Naive Bayes models is submodular. Also gives (1-1/e) hardness of approximation result for information gain in such models.
- [12] A. Krause, C. Guestrin, A. Gupta, and J. Kleinberg. Near-optimal sensor placements: Maximizing information while minimizing communication cost. In Proceedings of the Fifth International Symposium on Information Processing in Sensor Networks (IPSN), 2006
- Optimizing monotonic submodular functions subject to communication constraints.
- [13] A. Krause, B. McMahan, C. Guestrin, and A. Gupta. Selecting observations against adversarial objectives. In NIPS, 2007
- Maximizing the minimum over a set of monotonic submodular functions, with applications to robust experimental design.
- Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection